Adaptive CNN Filter Configuration for Variable Compute Budgets
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Solution Overview
Problem
Convolutional neural networks (CNNs) face challenges in resource-limited environments due to varying computational resources, leading to skipped frames and missed object recognition information, particularly critical in applications like autonomous driving.
Innovation Solution
An adaptive CNN architecture that adjusts the number of convolutional filters at each layer based on available computational resources, using shared filter coefficients across configurations to ensure consistent feature map resolution and efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CNNs are used for object recognition in resource-limited environments, then object recognition capability is improved, but computational resource consumption increases
Solution Approach 1:
The patent implements dynamic adaptation of CNN configurations by adjusting the number of convolutional filters based on available computational resources. The system monitors resource availability and dynamically selects appropriate CNN configurations, enabling the network to operate efficiently across varying resource conditions while maintaining reliable object recognition capability.
Solution Approach 2:
The patent changes key parameters of the CNN architecture, specifically the number of convolutional filters at each layer, to adapt to different computational budgets. By training multiple configurations with shared filter coefficients and selecting the appropriate configuration based on available resources, the system achieves both high recognition capability and efficient resource utilization.
2Use of energy by moving object
If computational budget is reduced to save resources, then energy consumption is improved, but object recognition accuracy deteriorates
Solution Approach 1:
The patent creates a universal CNN architecture that can function across multiple computational budgets. By training a single set of filter coefficients that work across different configuration levels, the system achieves both high accuracy at full budget and acceptable accuracy at reduced budgets, eliminating the need for separate models for different resource conditions.
Solution Approach 2:
The patent segments the CNN architecture into configurable layers with adjustable numbers of filters. Each layer can be independently configured to match available computational resources, allowing the system to partition the computational workload appropriately while maintaining overall recognition accuracy through shared filter coefficients.
3Speed
If frames are skipped to handle variable computational resources, then processing speed is improved, but information completeness deteriorates
Solution Approach 1:
The patent enables dynamic adjustment of processing capability for each frame based on real-time resource availability. Instead of skipping frames, the system adapts the CNN configuration on-the-fly for each frame processing decision, ensuring that no important information is missed while maintaining processing speed by using lighter configurations when resources are constrained.
Data Source
AI summary
Techniques related to object detection using an adaptive convolutional neural network (CNN) are discussed. Such techniques include applying one of multiple configurations of the CNN to input image data in response to an available computational resources for processing the input image data.